1. Field of the Disclosure
The present disclose generally relates to the field of orthorectification. More particularly, the present disclosure relates to various systems, methods, and media for performing sensor model refinement of satellite imagery, and for analyzing, evaluating, and compressing satellite imagery.
2. Background Information
Geometric accuracies for space-borne commercial imaging sensors have improved from 10 meters or more for a single stereo pair of images prior to 2008 to approximately 3.4 meters accuracy for GeoEye-1 images and 3.5 to 5 meters accuracy for Worldview-2 images. GeoEye-1, Worldview-2 Skybox-1, and soon Worldview-3 represent the current generation of United States commercial space imaging satellites.
The absolute geolocation, georeference accuracy of GeoEye-1 was measured, improved, and reported by GeoEye's Kohm and Mulawa in 2009, 2010, and 2011. The GeoEye-1 accuracies were validated by Fraser from the University of Melbourne in 2011. Mulawa showed that GeoEye-1 intra-image geometric accuracy, e.g., within an image, is +/−1 meter across the 15 kilometer swath. This accuracy was achieved through geometric calibration while on-orbit using repeated measurements at multiple sites in the United States and worldwide. The key finding was that for 64 or so images, the error of the means were ˜1 meter in the vertical and horizontal.
Absolute geolocation can be obtained from a large number of images to better than 1 meter absolute, in x, y, and z directions, from the camera model Rational Polynomial Coefficients (RPCs) in the image data without Ground Control Points (GCPs), anywhere in the image. Fraser confirmed GeoEye-1 accuracy measurements, and found that absolute Geolocation error, at 95% probability (2-sigma) of 20 centimeters in x and y directions, and 50 centimeters in z direction is possible with some effort using bias corrected RPCs and a single GCP.
There are multiple other methods for achieving, and slightly exceeding, the accuracies described by Fraser, using error-weighted means, triangulation from distant GCPs, or the implementation of co-registration using bundle adjustment, with correlation and spatially-variable pointing adjustment, also called Field Angle Mapping (FAM).
Leprince et al. provided a technique, and software, for co-registering images to sub-pixel accuracy, and provide bundle adjustment to improve image co-registration and geolocation accuracy. The capability allows co-registration of images to better than 1/50 pixel accuracy, if images are taken from the same angle and if the surface geometry for the scenes are perfectly known.
Mitchell showed in 2009 that GeoEye-1 Satellite stereo-image pairs can be processed to extract Digital Surface Models (DSMs), “processing 50 cm GeoEye-1 stereo satellite photos to 1 m Digital Elevation Models (DSMs) with vertical accuracies of better than 50 cm RMSE, as determined by thousands of ground survey points on mapping projects in Eritrea and Mexico.” Korb et al. demonstrated in 2012 that point clouds and DSMs could be extracted in urban areas at 0.8 meter resolution or better, and 0.1-0.2 meter precision from 16 GeoEye-1 images.
Super-resolution processing, a current topic in PhD dissertations in applied math departments, can improve resolution and signal-to-noise (SNR) by combining information from many lower-resolution images with rigorous geometric co-registration. Vandewalle, Su, and Boreman et al. describe that spatial resolution can be improved by three-fold or four-fold, an improvement of 1.58 to 2 National Image Interpretability Rating Scale (NIIRS), where the improved NIIRS=3.32*log10 (resolution improvement).
Boreman's PhD thesis and survey article, under Stevenson at Notre Dame, presented the state of the art of super-resolution processing using multiple images. A three-fold improvement in resolution is expected, but Su provides examples of four-fold improvement in resolution from a small group images.
Most imagery analysis systems work on single two-dimensional (2-D) images. As a result of error in known surface attitude, the difference between true orientation and horizontal, there are large uncertainties and errors in measured bidirectional reflectance distribution function (BRDF) reflectivity and emissivity, resulting from attitude-knowledge-error. Further, some remote sensing problems, such as characterizing material identification and temperature/emissivity, are fundamentally under-determined, e.g., have more unknowns than measurements, which require additional constraints or a priori knowledge, which reduces or limits accuracy obtained from a single image or measurement.
Most change detection algorithms work on 2-D imagery or datasets. As a result, change detection is limited by poor knowledge of surface orientations. The work of Mundy et al. is fundamentally based on use of three-dimensional (3-D) geometry, which can provide additional accuracy for both remote sensing and change detection. Mundy et al. use a Gaussian mixture model that fundamentally limits the accuracy of spectroradiometric exploitation, because the radiometry does not conform to either a Lambertian model or a BRDF-formulation reflectivity model as proposed by Hapke or others. In geometric change detection, the Mundy work is probabilistic, rather than deterministic as proposed herein. Mundy et al. use voxel and/or octree data models. Voxel and octree data models require more data storage space than 2-D images.